System and method for dynamic knowledge construction

ABSTRACT

A system and method responsive to input stimuli is provided by incorporating a computer software program, hardware processing engine, or a specialized ASIC chip processor apparatus to capture concurrent inputs that are responsive to training stimulation, store a model representing a synthesis of the captured inputs, and use the stored model to generate outputs in response to real-world stimulation. Human user forced-choice approval/disapproval generated descriptions and decisions may be dynamically mapped with conventionally presented information and sensor and control data. The model mapping is stored into and out of a conventional mass storage device, such as is used in a relational database for use in generating a response to the stimuli. By accessing commonly stored mappings, the system can be incorporated into a mixture of multiple domains and disciplines of users and can create a common understanding of knowledge and design concept contained within it through mutual interaction, and subsequent automatic modifications to a common relational database. The system and method is applicable to conventional storage and presentation devices, making it easily incorporated into a variety of commercial products, utilizing current commercial human-machine interfaces (e.g. Human-Machine Interface graphical user interface, or Graphical User Interface) and current mass storage devices. The system uses N-dimensional descriptions of observations and concepts in an infinitely expandable space, embracing elements of human thought. This allows the user to tailor this system to control operation of automated devices and appliances to reflect the individual&#39;s wishes and desires as a dynamic representation and mapping of user descriptions and decisions with information, sensor data, and device controls.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of application Ser. No. 09/658,275,filed Sep. 8, 2000, which claims priority from provisional applicationSer. No. 60/215,762 filed on Jun. 30, 2000. The contents of theseapplication are incorporated herein in their entirety.

This application is related by subject matter to application Ser. No.09/658,276, filed Sep. 8, 2000, now abandoned, the contents of which areincorporated herein.

TECHNICAL FIELD

The present invention relates to a system and method for dynamicknowledge construction.

BACKGROUND OF THE INVENTION

The development of Artificial Intelligence (AI) modeled humanunderstanding of the real world (RW), as summarized by Dreyfus et al.,“Why Computers May Never Think Like People,” Harvard Technology Review,42 (January 1986), is a collection of facts, rules of operation, andexperiences. This collection of symbolic information varies with humanskill in a particular application domain, by varying across a skilllevel scale from novice, through advanced beginner, and on to competent,proficient, and expert abilities. The variation of skill proceeds fromthe collection of facts to goal based action, with intuition andanalysis resulting into the instant understanding of the expert. Thereis a response time associated with skill. Feigenbaum et al.,“Signal-To-Symbol Transformation: HASP/SIAP Case Study,” AI Magazine, 23(Spring 1982) referred to the collection of RW data as a signaldetection process. The construction of this data into symbols creates amodeled symbol-structure, composed of low-level attribute elements beingclustered and grouped by RW objects (RWO) into an application hierarchy.

This process of construction was called an inference between knowledgesources and observations using an IF-THEN hypothesis-testing algorithm.Inferencing could use pattern invoked, heuristic methods or logical,tree-searched, rule-based methods. Here, the connection between RWO andthe modeled understanding is through an atomic object layer (with atleast one attribute or feature) and a detection process on the existenceof that attribute, with a probability of detection (P_(D)) and aprobability of false-alarm detection (P_(FA)). These atomic elements arebuilt into more complex structures of an object diagram (OD). Solinsky,“An Artificial Intelligence Perspective on the Sonar Problem—RecognitionControl Strategy in a Relational Data Base,” #T85-1199/301, RockwellInternational, Anaheim, Calif. (October 1985) showed that a relationaldatabase (RDB) was a means of constructing this knowledge base (KB) asthat of clustering detected feature values of modeled RWOs. Here theexample RWO is a tuple, and the fields of the RDB are the populatedfeature values.

An important element of RDB access in this application is shown by Li, APROLOG Database System, Research Studies Press Ltd., John Wiley, NY,N.Y. (1984) to incorporate predicate calculus. Feature detection is theprocess of signal detection and feature extraction through algorithmicprocessing of atomic level sensor measurements using “a priori”knowledge, including a detection threshold setting value (d). Just as inthe human skill model, an observation time (T) is required before adetection event can occur, and hence the symbolic construction processis discretized in time. Solinsky, “A Man/Machine Performance Model forAnalyzing Sonar System Design”, #T86, Rockwell International, Anaheim,Calif. (December 1986) showed that the information rate of this symbolicconstruction process is constant, but varies in resolution analysisbetween sorting Yes/No decisions at low resolution, and analysis at highresolution, and involves feedback over a fixed construction/extractiontime using short term and long term memory models (STM, LTM). Aftersymbolic construction, a sequence of atomic events can be used to retaintemporal or spatial event clustering (as in object spatial movement oreye-scanning motion).

The false alarm rate (FAR) in feature-detection is the ratio of P_(FA)to T. The process of matching a set of detected object features to a newset of features is that of classification, and Solinsky, “The Use ofExpert Systems in Machine Vision Recognition,” VISION'86 Conference,Detroit, Mich., 4-139 (June 1986) showed that the Expert System (ES)paradigm was applicable to this process in vision applications. Thefeedback of the setting of the threshold detection value “d” is providedby the speed of the clustering processes required. This method ofthreshold controlled decision process performance is based on one offive methods described later by Klir, Advances in Computers, Vol. 36edited by M. C. Yovits, Academic Press, NY, N.Y., 255, (1993).

The use of object detection in visual tasks is a human expert skillimplemented as a Gestalt process. Solinsky has generalized the approachfor a) feature selection (see Solinsky, “A Generalized FeatureExtraction Approach,” VISION '87 Conference, Detroit, Mich., 8-57, June1987), b) classification using neural-networks (see Solinsky, “MachineVision Tutored Learning Using Artificial Neural Systems Classification,”VISION '88 Conference, Detroit, Mich., 1-MS88-490, June 1988), and c)decision processes for low FAR regimes (see Solinsky, “Evaluating SystemPerformance in Low False Alarm Rate Regimes,” #JCS/ASD/92-001, AdvancedSystem Division, SAIC, La Jolla, Calif., February 1992). Solinsky, “AMethod for Compact Information Characterization in a Finite, DiscreteData Set,” #JCS/ASD/93-003, Advanced Systems Division, SAIC, La Jolla,Calif. (April 1993) has also presented generalized informationstructuring for characterizing a discrete data set in a largedimensional feature space. This technique uses an important analysismethod termed a “balloon matrix” for measuring correlation inN-dimensional hyperspaces. The hyperspace “diameter” of the balloon isfound in a search time, which is related to the correlation lag and thecorrelation order. The compact form of a subspace representation uses afractal dimensional ordering. In this way a much larger, and eveninfinite space can be made into a compact, set of finite spaces.Solinsky, et al, “Higher-order Statistical Application in Acoustics withReference to Nonlinearities in Chaos,” Third International Symposium onSignal Processing Applications (HOSSPA 92), Gold Coast, Queensland,Australia (August 1992) has shown higher order statistical (HOS)correlation to exist in acoustic data from mammals and in neural datafrom bat brain neurons. Solinsky, et. al, “Signal Analysis of NonlinearDynamics and Higher-Order Statistics,” SPIE Proceed. 2037Chaos/Dynamics, San Diego, Calif. 163 (July 1993) has shown this HOScorrelation to be present in other data set applications includingfinancial data. This HOS correlation represents the Gestalt process ofthe N-dimensional hyperspace correlation (for N>2).

The structure for knowledge representation in AI has been dominated bythe object model, with linked object diagrams (OD), such as shown byBooch, Object-Oriented Analysis and Design, Benjamin/Cummings Pub. Co.,Redwood City, Calif. (1994) with class attribute relationships, Coad andYourdon, Object-Oriented Design, Prentice Hall Inc., Englewood Cliffs,N.J. (1991) for super/sub object and associated object relationships,and the current modeling using the Universal Modeling Language (UML) byFowler and Scott, UML Distilled, CRC Press, Addison Wesley Langman,Inc., Menlo Park, Calif. (2000), Rumbaugh, Jacobson and Booch, The UMLReference Manual, Addison-Wesley Langman, Inc., Menlo Park, Calif.(1999), and Booch, Rumbaugh, and Jacobson, The UML User Guide, AddisonWesley Langman, Inc., Menlo Park, Calif. (1999).

The construction of information systems that are intelligent, by beingself-erecting, and capable of implementation in a computing machine, wasexplored by Solinsky, “Intelligent Information Systems,” SAIC WhitePaper, (February 1995). Here the use of human interaction is used toconstruct information from data, as defined as “assessment by methodsunrelated to the data itself.” Intelligent systems have the ability toacquire and use information. These systems evolve information from datacases using an information “channel” of flow similar to the previouslymodeled information rate shown earlier by Solinsky (December 1986) to belimited. The process in the intelligent information system usingcomputing machinery involves a set of metrics:

-   1) The information has a finite lifetime (τ) and channel length L,    which because of a fixed flow rate, c, limits the ability of the    system to achieve instantaneous synchronization. Rather, a unit of    synchronicity is finite, and is in terms of a “chron” (crn≡cτ/L).-   2) The distribution of information through replication must have a    user's transaction cost (C_(R)) to limit resource wasting and time    consuming processes.-   3) The system must incorporate a feedback process to minimize the    average cm unit in the processes ( crn≡{crn}_(min)).-   4) The system customizes the user interaction to minimize the user's    emotional level as determined through an interface (e.g., Gelemter,    “The Muse in the Machine,” Free Press Division of MacMillian, Inc.,    NY, N.Y. (1994)).-   5) Diverse user interaction creates information latency, which    incurs a cost (C_(L)) that randomizes information through    equivocation (Solinsky (December 1986)).-   6) Diverse user interaction also creates information uncertainty as    an injection of information randomness or noise (Solinsky (December    1986)).-   7) User feedback is used to retain information accuracy by    consistent and frequent decision confirmation.-   8) Both object-modeled information flow (e.g., OD) and data flow    must exist in the system, where the information rate channel    capacity (CC) limits the information flow rate, and the computing    hardware channel bandwidth (BW) limits the data rate. This process    involves a user decision of transforming unknown data into objects    for investigation and analysis (e.g., unfamiliar objects are data    flow). Known objects are discarded by the user (e.g., familiar    objects are information flow).-   9) The user actions are modeled by two levels of memory, STM and    LTMs with the LTM model of familiar objects being accessed through a    set of multileveled CC limited pathways. This provides an    implementation of situation awareness for a specific user    interaction.-   10) The system incorporates an overall cost function (CF) which    limits the use of information flow by a redundancy factor (Re for    Re=1.0 as no information flow, and Re=0.0 as an infinite flow for a    state of confusion). This CF is a nonlinear, direct mapping of Re,    and is inversely mapped to the information rate CC. The CF is user    and application dependent. The process of transforming    data-to-objects is modeled as human perception and transforming    objects-to-data is modeled as human instantiation.

A series of research proposals (Murphy and Solinsky, “Automated ModelCorrelator and Metamodel Building Environments,” Accord Solutions SBIRProposal A95-065 (January 1995), “The Information Computer—AnIntelligent Systems Component for Consistent Abstraction of CollaboratorExperience,” Accord Solutions (1996), and Accord Solutions ATP proposal“Components for a Concurrent Paradigm,” (May 1997)) addressed theapplication of this information construction structure into a series ofdecision surfaces based on information content in the analyzed data. Aseries of briefings on the information system elements (Solinsky andMurphy, “The Information Computer”, Accord Solutions briefing presentedfrom 8/95-2/96 to McDonnell Douglas Corp. and Cubic Corp.) identified animportant concept in intelligent information construction systems, whichcan be implemented in computing machines. This information computer (IC)consists of:

-   1) A LTM representation of information as a linked set of objects in    an OD, with ordering based on the user's viewpoint, with objects of    importance being closest, and objects of less importance being    furthest away in “distance” (as a link count) from the most    important object to the user.-   2) The OD viewpoint allows objects of self-containment to be chunked    into a single, macro object, which has links to other objects, but    is fully represented in the user's viewpoint context as a single    macro object. In this way the entropy H, which is a function of the    OD object count (O), link count (L), and average hyperspace spanning    vector ({tilde over (D)}) is defined as H≡F (O,L, {tilde over (D)})    and is minimized by chunking. In many instances, the chunked objects    can capture a subspace of the general OD attribute set.-   3) The user decision/response is modeled as one of three outcomes of    unfamiliar objects: a) discarded as not of interest, b) modified    using STM to be corrected, and c) identified as being new, and    entered into LTM. Familiar objects are automatically responded to    with minimal time and effort as an automatic response. The use of a    forced-choice interaction model with the user is a cognitive science    method of creating confusion in the representation to force the user    into a reactive and possibly emotional response which accesses the    user's actual LTM information.-   4) The IC uses an Action Channel (AC) to construct confusion in the    representation of the OD confronting the user. The AC is similar to    a Shannon communication channel for data flow, except the AC    constricts the information flow as discussed earlier through    equivocation and uncertainty. A specific element of the AC is an    access to the OD stored in LTM, with the combination of new objects    randomly included as input to the channel. The channel itself    involves a type specified linking process (i.e., a verb) involving a    propagation time step, which changes the output OD, by adding    uncertainty through additional random links added and removed from    the OD as a noise process, and equivocation as an object removal    process to be on the order of the input object count in order to    retain the same entropy of input-to-output OD space.-   5) The AC model for OD modification is based on binary construction    to a single object by a link type change as    noun/verb/object/labeling for labels of: a) no change (NOP), b)    create a new forward link (p addition), c) combine two objects (y    combining), and d) create a backward link (b addition). The noun    element in the change is the identity of the original object of the    OD where the linking process occurs.

The proposals by Solinsky and Murphy extended the decision process toclassification techniques using a neural network decision process and ageneralized decision process as represented by Klir, Advances inComputers, Vol. 36 edited by M. C. Yovits, Academic Press, NY, N.Y.,255, (1993). The general decision process is an expansion ofP_(D)/P_(FA) decision in a typical likelihood, cost function format, andincludes a) classical set theory (Hartley, “Transmission ofInformation”, The Bell Systems Technical Journal 1, 535-563 (1928)); b)fuzzy set theory (Zadeh, “Fuzzy Sets”, Information and Control 8 (3)338-353 (1965)); c) probability theory (Shannon, “The MathematicalTheory of Communication,” The Bell Systems Technical Journal, 27,379-423, 623-656 (1948)); d) possibility theory (Zadeh, “Fuzzy Sets as aBasis for a Theory of Possibility,” Fuzzy Sets and Systems 1 (1), 3-28(1978)); and e) evidence theory (Shafer, A Mathematical Theory ofEvidence, Princeton University Press, Princeton, N.J. (1976) andDemster, “Upper and Lower Probability Inferences Based on a Sample froma Finite Univariate Population,” a) Biometrika 54, 515-528; b) Annals ofMathematical Statistics 38, 325-339 (1967) as bounded probability, andbelief functions (Shafer, “Belief Functions and Possibility Measures” inAnalysis of Fuzzy Information Vol 1 edited by J. C. Bezedek, CRC Press,Boca Raton, Fla., 51-84 (1985))). Klir summarizes this decision processas being either involving fuzziness or ambiguity, where“strife-ambiguity” is a disagreement of alternatives and“nonspecified-ambiguity” is a set of unspecified alternatives, i.e.issues involved with decisions which require resolution because ofdisagreement or lack of information, e.g. at an emotional level.

The decision process of the IC was modeled to include Klir's min/maxuncertainty ranges of decisions to become invariant to uncertaintythrough the user decision process, such that the evolved LTM OD is thesame information from all user viewpoints which then becomes a commonset of information or knowledge.

This background work in AI and information modeling has not included thecombinations described for the IC and the use of neural-networks (NN) indecision processes of OD representations. Eliot, “Ruling NeuralNetworks,” AI Expert, 8 (February 1995) has shown that a NN is not aseasy to understand as an AI ES, which involves only rules. This isbecause ES logic involves discrete binary Yes/No states and NNs involvesigmoid-shaped decision surfaces with a focus on the “Maybe” region inthe Yes-to-No transition region of the sigmoid. While ES's attempted toinclude this as a user input/review process with a certainty factor indecisions, this approach was unsuccessful because its final output wasnot a complete decision. The lure of the ES rule-based modeling is itscompactness, but this can better be represented in a predicate calculusformat with NN decisions as incorporated into this invention. Thecurrent invention transforms the OD models and AI of the IC to amathematical hyperspace representation which is efficiently representedand operated on for RW applications using efficient bit-levelmanipulation and computation.

A series of patents have dealt with ODs and the use of object models inapplications, and particularly with RDB accessing. U.S. Pat. No.3,970,992 deals with a keyboard macro for retrieval application in adata processing system. U.S. Pat. No. 4,906,940 deals with a“rubberized” template matching approach for guiding an object on a road.U.S. Pat. No. 5,506,580 involves a data compression approach using acharacter stream library encoding. U.S. Pat. No. 5,548,755 is a hashingtechnique to optimize RDB-grouped query access. U.S. Pat. No. 5,586,218uses case-based reasoning for information gathering in a RW sensorderived data set. Here, decision and case construction use a GeneticAlgorithm decision process. U.S. Pat. No. 5,701,400 constructs atool-kit for financial advisors based on weighted logic ES, IF-THEN-ELSErules to data sets in a RDB.

U.S. Pat. No. 5,712,960 incorporates abductive reasoning as a metainterpreter for updating a communication data base management system.U.S. Pat. No. 5,768,586 uses an object structure for data configurationin system modeling of complex enterprises, which begins with high leveluser descriptions and constructs low level descriptions from the modeledprocess. U.S. Pat. No. 5,778,378 is a document retrieval application ofan object-oriented (OO) framework for word indexing and parsing. U.S.Pat. Nos. 5,790,116; 5,794,001, and 5,900,870 use a GUI to construct ahierarchical 5 definition of an object structure in a data recordapplication. Templates are used for selecting data fields linkable to acollection folder of instantiations. Various 2-D graphics are used, suchas a node-arc graph. U.S. Pat. No. 5,806,075 uses a triggeringmethodology based on data values, of data duplications between local andremote sites. U.S. Pat. No. 5,832,205 is a memory failure detectionprocess based on comparative instruction analysis. U.S. Pat. No.5,875,108 is a GUI interface intelligence through adaptive patternrecognition of historic actions as in a button pushing effort using aVCR remote control. U.S. Pat. No. 5,893,106 incorporates a serversupport to client users, which encapsulates a class hierarchy of 3-Dgraphics in data base applications. U.S. Pat. No. 5,905,855 correctserrors in computer systems by two state analysis of initial and finalstate reference points.

U.S. Pat. No. 5,911,581 incorporates a metric for determining mentalability of complex task solving, and models reaction time, awarenessthresholds, attention levels, information capacity and LTM access speed.U.S. Pat. No. 5,915,252 uses a consistency check between a data sourceand target to simplify a user's job in data transferring. It includesprotocol free construction of ES object links embodied in a common userinterface. U.S. Pat. No. 5,926,832 is a means of increasing memoryaccess by memory address analysis and storage. U.S. Pat. No. 5,936,860is an application to warehouse control functions by a user with OO datamodeling. U.S. Pat. No. 5,953,707 uses a planned decision model in aclient/server database from various user viewpoints in sales planningand inventory management as a user GUI without an OO model. U.S. Pat.No. 5,958,061 uses a cache to store states for instruction translation.U.S. Pat. No. 5,966,712 applies to the use of RDB storage ofbiomolecular sequences which, compares sequence frames and groups offrames and displays results to the user.

U.S. Pat. No. 5,970,482 applies to the application of feed-forwarddecisions to the data mining process. A predictive model is used tocompare and rank symbolic data correlation significance. U.S. Pat. No.5,978,790 uses an edge-labeled tree approach to match input elements tooutput restructuring in a semi-structured database. The tree can only bestructured in 2-D data base structures. U.S. Pat. No. 5,991,776 involvesan application of RDB indexing by linking tuple identifications to thedocument, but does not use an OO structure.

U.S. Pat. No. 5,995,958 manages a database through the use of acyclicgraphs by mapping an infinite data set to a finite storage of aλ-function core representation. It is applied to RDBs with user querysupport, but does not involve OO models, since the links are independentof the node contents. U.S. Pat. No. 5,999,940 incorporates a 2-D visualrepresentation for user discovery and visualization for applicationsinvolving multiple access to databases, such as in health and doctortreatment areas. U.S. Pat. No. 6,002,865 uses a multi-dimensional set ofspreadsheet pages to construct a database by multiple levels ofresolution. U.S. Pat. No. 6,003,024 deals with row selection of 2-Ddatabase accesses for attribute-based record selection as anintersection of attribute correlation in 2-D. U.S. Pat. No. 6,006,230involves a client/server remote user access application modeled in OOtechnology for proxy mapping.

U.S. Pat. No. 6,009,199 is a decision process for classification indecision trees. It is an iterative mapping of subspace to full spacebased on discriminative processes. The perceptron model is used inclassification.

The previously cited U.S. Pat. Nos. 5,832,205; 5,905,855; 5,926,832; and5,958,061 and U.S. Pat. No. 6,011,908 deal with the iterativetranslation of computer microprocessor instructions to a target set ofprocessor states embodied in a chip set with gated memory buffering. Theprocess speeds up the instruction execution as historic referencesoccur, and the system consequently becomes more adept at predicting thenext sequential executable instructions. This is an N=2 form ofcorrelation prediction.

SUMMARY OF THE INVENTION

The system and method described in the detailed description below areusable to, among other things, generate outputs in response to realworld stimulation. These outputs are generated by capturing concurrentinputs that are responsive to training stimulation, storing a modelrepresenting a synthesis of the captured inputs (that may be based on amathematical projection of a hyperspace-modeled OD), and using thestored model to generate outputs in response to real-world stimulation.The system and method may be used in a wide variety of human interactionapplications and apply to conventional storage and presentation devices,permitting the system and method to be easily incorporated into avariety of commercial products that utilize human-machine interfaces(e.g., HMI graphical user interface, or GUI) and current mass storagedevices. By way of example, not limitation, the system and method may beapplied to user presence and/or identification. In this case, theconcurrent inputs may be handwriting and speech. In one particularexample, the speech may be related to the handwriting such as when aperson speaks his or her name while at the same time writing his or hername. A model representing a synthesis of these concurrent handwritingand speech inputs is stored. The model may, for example, be a worldlineof linked object diagram exemplars in an N-dimensional space. The realworld stimulation may be concurrent handwriting and speech inputs thatare compared to the stored model, and the outputs (e.g., an identityverification) may be based on the results of the comparison. The use ofa mass storage device, such as but not limited to an RDB, linked to theOD model by projections and NN/TC decisions in a binary taxonomymapping, provides for a continuous expansion of each applicationcomplexity. The methodology of the present invention may be at leastpartly incorporated into a computer software program, a hardwareprocessing engine, a specialized hardware application specificintegrated circuit (ASIC) chip, or net list representations for avariety of ASIC technologies.

The system and method may further include a forced choice interactionthat generates one or more additional inputs that are captured andincorporated into the model. In the case of user identification, theforced choice interaction increases the probability of correct useridentification, and can include measurements of user emotions and/orstress. For user identification, the forced choice interaction generatesspeech inputs that are responsive to a display of one or more imagesthat may each include one or more letters, characters, numbers, symbols,images, etc. and combinations thereof.

The present invention is described in the context of illustrativeembodiments. A key concept of the invention is that the user'sshort-term and long-term memory is captured into an algorithmicframework by interaction with the user during “training” which thenunderstands the decisive needs and wishes of each individual, and isable to dynamically map this information to low-level control functionsused by commercial devices or to express this information to other usersby using a common, intermediate method of information storage. Thismapping uses a linked-object model in an infinitely expandablehyperspace (implemented as a bit-level computation), with synthesiscreation based in user forced-choice input, and projected densityderived, HOS feature comparison. By using a short and long-term storageapproach, the utility of the concept is extendable to many applicationswith increasing complexity, with expandability only limited by thephysical storage devices and communication hardware bandwidths.

The complexity and expandability of user interaction can vary for eachapplication, as does the variation and extent of the resourcessupporting this interaction. The simplest embodiment might be anapparent intelligent interaction response to the user with homeappliances, which is through a single interface, far more advanced thana collection of remote control units (e.g. VCR remote controls, garagedoor opener, remote telephone extension, etc.). A numeric scale torepresent this complexity (dubbed the Accord Scale, from 1 to 10 inlogarithmic value; note that there is no value of “0” which wouldrepresent the complexity of N=1, or a VCR control) is illustrated inTable 1. The OD entropy is a key element in the formulation of theAccord Scale complexity.

TABLE 1 Accord Scale O.D. Space Numeric Dimension Complex- Value (N) ityApplication 1 10 Low Commercial electronic signature verification forsingle user. 2 50 Moderate Internet music source search engine forsingle user finding of an MP3 music selection of current tastes,independent of type, artist, musical instrument choice, etc. 5 100,000Medium Economic prediction models based on a number of users,cooperative- ly interacting with historic and globally dynamicdatabases, as developed from long-term memory. 7 500,000,000 VeryRepresentation of the human High genome code for a variety of healthconditions compounded across multiple symptoms from many medical historyexamples with a utility in singular drug therapy.

The specific examples are meant to show the growth in complexity of theuser count, the historic and developed database encapsulation developedfor each application, and the complexity of each output requirement.Note that the scale of N is not necessarily an OD volumetric growth,since the invention uses a more linear linking of the hyperspace. Theseexamples are for illustrative purposes and a detailed discussion ofelectronic-signature verification is provided in the detaileddescription below.

None of this prior art involves access in information structures fordimensions N>3 or incorporates adaptive decision processes usingneural-networks (NN) as a form of N-dimensional cell correlationrepresentation. None of these approaches in information construction,organization, or prediction, incorporate higher-order correlation orHOS, and avoids the common AI ES, IF-THEN-ELSE, and logic-treeconstructions. All template matching and correlation of this prior artare restricted to 2-D, linear techniques. None of this prior artincorporates an AC for user information testing with emotional sensingto construct common-information knowledge in a LTM, and also createconfusion in a STM forced-choice decision process. None of this priorart incorporates the metrics of the IC in information flow parameters ofrate c, channel capacity (CC), and information system length L and thetemporal parameters of τ, crn , crn, T, and cost functions C_(R), C_(L),CF and redundancy function Re. None of this prior art uses entropy, H,as a metric function of the OD representation vector space spanningdistance {tilde over (D)}, for controlling analysis resolution, nor doesit use predicate calculus for link location in a RDB. None of this priorart simplifies the OD model to a set of points in an N-dimensionalspace, which is expandable through an extension to the LTM storage ofprojection operators and decision surfaces, rather than explicitexamples through feature attributes. By using a partially correlatedaxis space, which can be fractal in nature, this representation isexpandable through subspace constructions at any later point in time,without loss of original model parameters. This invention addresses theoriginal RDB constructions and avoids the approximations used in SQLdevelopment. The invention incorporates the simultaneousness of theinput features in an N-D spatial correlation for a unique utility andstorage of human and sensor information.

The invention can be applied to human interaction applications that arenot represented in N=2 dimensional methods such as drawings, graphics,spreadsheets or database queries. These human interaction applicationsinvolve a complexity of dimensional correlation for very large N, whichis beyond second order correlation and linear processes of N=2applications and instead the correlation is of a concurrent, nonlinearorder N, as in Gestalt thinking. This concurrency is described throughHOS representations and synthesis of the model. The system is a uniquecapability for information storage and retrieval which is beyondconventional graphic description for review, and is fundamentally userand hierarchically independent.

These and many other advantages of the present invention will be morecompletely understood and appreciated by careful study of the followingmore detailed description of illustrative embodiments of the inventiontaken in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows the invention application concept of user interactionthrough a simple device with other users, control functions, and anextension of the hand-held device to increasing resources embodied as acomputer server device.

FIG. 2 shows the electronic-signature verification application exampleon the Accord Scale of complexity value 1, and involves a complexity inmulti-channel user inputs for simultaneous correlation in electronicidentification.

FIG. 3 details the invention system interfaces for a generalapplication, with real world object sensing and control, and human userinterfaces, with the fundamental mapping to a Core Model and a MassStorage Device.

FIG. 4 details the Core Model elements of finite resource dimensionsfrom input s M′ dimension and storage device M dimension, from theinfinitely expandable N space of the application complexity.

FIG. 5 details a simple 2-D example of mapping an OD space of objectlocations and links to projected densities into lower dimensionalsubspaces which are characterized by density moments, here shown forN=1.

FIG. 6 details an example of the subspace mapping expansion from N=5,down to the simple 1-D densities of FIG. 5.

FIG. 7 details the use of the invention technology derived from thisinvention in the electronic-signature verification application of FIG.2.

FIG. 8 details a flow chart of the invention method.

FIG. 9 details a generic block diagram for the invention system.

GLOSSARY

For ease of reference, the following glossary provides definitions forthe various abbreviations and notations used in this patent application.

AI Artificial intelligence

HMI Human-Machine interface

GUI Graphic user interface

RW Real world

RWO Real world objects

P_(D) Probability of detection

P_(FA) Probability of false alarm

RDB Relational database

SQL Structured query language

KB Knowledge base

d Detection threshold setting value

T Observation time

STM Short-term memory

LTM Long-term memory

FAR False alarm rate

ES Expert system

HOS Higher-order statistics

OD Object diagram

UML Universal Modeling Language

τ Information lifetime

L Information channel length

c Information channel flow rate

crn Temporal synchronicity unit or “chron”

C_(R) Information replication cost function

crn Average crn unit minimized by feedback

C_(L) Information latency cost function

CC Information rate channel capacity

BW Hardware channel data rate bandwidth

CF Cost function limits on information flow

Re Redundancy factor

IC Information computer

H IC entropy of OD

O IC object count in OD

{tilde over (D)} IC OD hyperspace spanning distance vector

L IC OD link count in OD

F ( ) Function of contained elements

AC IC action channel

NOP No operation change in AC to OD for iteration cycle

p AC labeling for forward link addition in AC to OD

y AC labeling for combined link in AC to OD

b AC labeling for backward link addition in AC to OD

NN Neural-networks (form of decision mapping)

OO Object-oriented

M, M′ Finite dimensional OD space for LTM mass storage (M) and RWinterfaces (M′)

N Infinite dimensional OD space for STM decision processes

n N-D space attribute axes

n_(i) i^(th) attribute space axis

i Location count index of n_(i) axis

x₁,x₂ 2-D example of attribute axes

d _(o) Location vector of o^(th) index object exemplars in n attributespace

o Object index in OD space

O Total count of o

{tilde over (P)}_(i) Hyperspace 1-D profile projector onto the i^(th)axis of OD object locations

P₁, P₂ Histogram representation of {tilde over (P)}_(i) projectedprofiles for axes x₁, x₂

l _(mn) Hyperspace link vector between objects m and n of OD

{tilde over (L)}_(i) Hyperspace 1-D profile projector onto the i^(th)axis of OD link locations

L₁, L₂ Histogram representation of projected profiles {tilde over(L)}_(i) for axes x₁, x₂

C_(ō) Class “c” distinction of subset of objects in vector ō

l 1-D index of link vectors l _(mn)

L_(l) Total count of index l

N′ Subspace finite dimension count of OD from N space dimensions of OD

m Specific example set of dimensions in subspace count M (here used forN′)

{tilde over (P)} _(m) , {tilde over (L)} _(m) Hyperspace M-D profileprojectors for object and link densities to subspace m from OD space n>m

P _(m) , L _(m) Histogram representation of profile projectors {tildeover (P)} _(m) , {tilde over (L)} _(m) for axes m

{tilde over (P)} _(m) ^(c), {tilde over (L)} _(m) ^(c) Hyperspace M-Dprofile projectors for object and link densities to class-filteredobject subset “c” in subspace M from OD space n and m and c<O.

P_(m) ^(c), L _(m) ^(c) Histogram representation of profile projectors{tilde over (P)} _(m) ^(c), {tilde over (L)} _(m) ^(c) for axes m

A_(m) Central moments of 1-D population density of order m

A_(o) Total count of projected profile 1-D density

a Area A_(o) for M=1 space projection

A₁ Centroid mean

μ Mean A₁ for M=1 space projection

A₂ Centroid (area normalized) second central moment

σ² Variance central moment of A₂ for M=1 space projection

A₃ Centroid (area normalized) third central moment

m₃ Skewness central moment of A₃ for M=1 space projection

A₄ Centroid (area normalized) fourth central moment

M₄ Kurtosis central moment of A₄ for M=1 space projection

d_(c) Simple class distance metric used in constructing class decisionsurfaces of second-order statistical densities (i.e. Gaussian).

S Normalized m₃ moment (i.e. S=0 for Gaussian)

K Normalized m₄ moment with excess correction to Gaussian (i.e. K=0 forGaussian)

MV Machine vision

SD Subspace diversity variable based on N and N′ for each N′ example

ρ( x _(m)) Image pixel density of subspace dimension m as a function ofdimensional absolute distance x _(m). Used in computing moments and is ahigher dimensional representation of FIG. 3 histograms.

A₀ ^(m), A₁ ^(m), A₂ ^(m), A₃ ^(m), A₄ ^(m) Extension of A_(m) form=0,1,2,3,4 to m-D subspace central moment density weightedcomputations.

S^(m), K^(m) Extension of S and K to m-D subspace central moment densityweighted computations

TC Terminal cycle (form of decision mapping)

S(i) Step operator value of random table lookup function from “pixel”value input seed at location address “i” in TC mapping

S _(n) Location vector of addresses generated in n dimensional spacebased on TC sequences using step operator function values S(i) for astable TC “orbit”

ε_(nm) Eccentricity of major and minor axis in 2-D (n,m) representations

LAN Local-area network

GPS Global Positioning System

RF Radio frequency

ASIC Application-specific integrated circuit

LPC Linear predictive coding

ID Identification

Y Count of TC iterations

DETAILED DESCRIPTION OF EXAMPLE EMBODIMENTS

The invention is described with reference to a typical exampleapplication as an intelligent interface between two classes of elementsused in many product-based application devices as shown in the inventionapplication concept of FIG. 1. FIG. 1 shows a human user 100 interactingvia a hand-held unit 102 with other users 104, as in a communicationsystem, and with other devices 106, as in a television control system.This interaction is two-way: as in speaking into a microphone 108 ofhand-held unit 102 (e.g. “say”), or punching keys on the unit's keyboard110, or moving in front of the unit's camera 112; and as in readingnumbers displayed on the hand-held unit's display 114 (e.g. “see”). Thehuman user 100 is able to communicate with other users 104 and thecontrolled devices 106 through a tailored, dynamic interface that isbased on the short-term (as in current and recent thoughts) andlong-term (as in past and remembered thoughts) user's dynamic memoryrecollections. The hand-held unit 102 has a built-in memory (not shownin FIG. 1) that stores the specific user(s) preferred interactionmethods as a short-term memory, and has additional memory to extend thismapping via a connection to a network server 116 for long-term memorystorage. This extended mapping (networked database) contains informationfrom multiple users, and in itself captures commonality among the userinteractions.

A specific example application of this invention concept is shown inFIG. 2 for electronic signature verification and specific user presenceconfirmation, dubbed e-presence. Here, the user is using a simple,commercial input device, such as a Personal Digital Assistant (PDA) 10with a stylus pressure keypad and a microphone input. The microphone 1is used to acquire input data from the user 4 speaking one's name (asindicated by the line associated with reference numeral 5), at the sametime that one is signing one's name by hand 6 on a signature pad 2 usinga stylus 3. In familiar three-dimensional space, a person or object canbe located by specifying three coordinates. In an x-, y-, z-space, thex-, y-, and z-coordinates are specified. In accordance with the presentinvention, additional axes are added to generate an N-dimensional space.For example, one additional axis may represent time, another additionalaxis may represent temperature, etc. The current x position, the currenty position, and the current z position, the current time, the currenttemperature, etc. define a particular point within this N-dimensionalspace. The changes over time of x position, y position, z position,time, temperature, etc. define a path or a “worldline”. The correlatedspeak/sign signature verification constructs unique user identificationof attribute space dimension N=10 (5 signature features and 5aural/spoken features) and consists of a linked OD in the inventiontechnology for comparison to previous user “training” signatures. Thisfirst phase of the e-presence application is not just using signaturerecognition, as might be in a thumb print reader, and susceptible toforgery or facsimile input, nor is it just using aural identification ofthe user's voice, which could be compromised by using a tape-recordedspoken name. Rather, the e-presence system is capturing a dynamic eventof a speak/sign correlation of motions and actions, which is expressedin all the manners of the specific user via the N=10 dimensional spaceconcurrency. The time sequence of this speak/sign process creates a“worldline” of linked OD exemplars in the ten-dimensional space. Ofcourse, speak/sign are merely examples of concurrent inputs that areresponsive to training stimulation and the scope of the invention is notlimited in this respect. In fact, the invention could apply to aninterface for a severely handicapped person, whose biometric inputsmight be just a flutter of eyelids in front of an eyepiece camera. Also,the invention is not limited to two concurrent inputs and is generallyapplicable to two or more concurrent inputs. The number and type ofconcurrent inputs will generally depend on the particular applicationdeveloped in accordance with the inventive concepts described herein.

It is expected that because of this high dimensionality, the first phaseof the e-presence process of N=10 will have a probability of correct(Pc) user ID after only a few training sessions (e.g. less than ten),and will be Pc≧95%. However, this is not at all sufficient forelectronic signature applications in commerce, and so a second phase ofe-presence involves the use of a sequenced display 7 of the PDA 10.Here, a set of letters (or numbers) are sequentially shown to the userto see (as indicated by the line associated with the reference numeral8) each letter, one at a time, which, after each display, one is askedto say (as indicated by the line associated with the reference numeral9) the displayed letter name. The rate of display is based on aforced-choice user mode (on the order of five per second), and includesthe dynamics of the sequence. While a set of letters are sequentiallyshown in this example, the sequenced display 7 may generally be imagesthat may each include one or more letters, characters, numbers, symbols,images, etc. and combinations thereof.

Thus, the see/say part of the second phase includes a dynamicrecognition of again an N=10 attribute representation, where here allthe attributes are based on the aural spoken sounds of a known-displayedletter. The sequenced exemplars create a second user worldline. The Pcfor this second phase event is expected to be Pc>90%. Because thedisplayed letters are random and change each time the system is used,the false detection rate here is expected to be much lower, resulting ina combined first and second phase of e-presence to be a combinedPc>98.5%.

This example application may be further embellished by includingincreased complexity of failed first phase testing, and user dynamicprofile updating as well as stress detection and emotional sensingoverrides. In summary, the e-presence application precedes through thesteps of Table 2.

TABLE 2 e-presence Application Steps Action N Pc Pc 1 Sign/Speak (5aural; 5 95% or <95% (increase signature) complexity) 2 Repeat 1 if Pc<95% (10 aural; 10 98% or <98% (stop) signature) 3 See/Say (10 aural)90% or <90% (increase complexity) 4 Repeat 3 if Pc <90% (20 aural) 90%or <90% (stop) 5 Accept IdentificationThe invention is further detailed in this application after thefollowing initial fundamental description of the technology.

FIG. 3 shows the key elements of the application concept shown in FIG. 1and the example e-presence application shown in FIG. 2. FIG. 3 shows ameans of interfacing a general purpose user and sensor/effector-basedcommercial device to a relational database (RDB) mass storage devicewhich provides an isomorphic mapping of human knowledge interaction fordynamic intelligent responses that are independent of the applicationdomain (or multiple domains) or user (or multiple user) specifics. Theadvantages of this approach over the prior art are that the simplisticuser and RW interfaces can create a concurrency of stimulations in ahigh dimensional space for more precise modeling, which is also storableusing a simplistic 2-D or 3-D mass storage method. The first class ofelements shown in the figure is for user and sensor interfaces. Theuser-receiving interface 11 includes data outputs in the form of visualdisplays in graphics or text, aural as sounds or speech, and kinestheticas vibration or thermal/electrical stimulation. The user transmittingdata inputs interface 11 includes data inputs in the form ofkinesthetic-controlled device elements such as mice, keyboards, gloves,or joysticks; chemical/odor elements as in human odors or skinmoisture/sweating; and spoken sounds or word elements. In addition,other RW sensor inputs 12 are used in the example interfaces, which caninclude, but are not limited to electronic camera/video data, GPSlocation sensing, remote thermal sensing of human elements in scanned orimage data, and possibly switch based element motion. Finally, RWcontrol output devices 12, such as lever arms, motors, and robotics areused as effector devices.

The second class of elements shown in FIG. 3 and used in these sameapplications are for the mass data storage devices 13, which can be thecomputer server shown in FIG. 1 and can be constructed of typicalread/write functionality in magnetic, magneto-optic, random accessmemory (RAM), flash electronic memory, and any other electronic, oroptical/electro-mechanical devices capable of storing and retrievingdata values in digital format (bits, bytes, words), which are inherentlyin a physical structure and have an access format in two dimensions (assurface cells or surface locations) or in three dimensions (asvolumetric cells such as holographic optical access or collections ofsurface locations, such as disk platters). These interface structuraldimensions can be summarized as linking a LTM, physically contained in alow-dimensioned, physically constructed mass storage device 13 (i.e.with a finite dimension ≧3), to a RW access method involving human andsensor/control interactions of various attributes and also being offinite dimensions 11, 12.

The system and method constructs three properties of algorithmicfunction, also shown in FIG. 3, that include a Core Model 14, and twomechanisms for input and output mapping to the Core Model. The firstmechanism is a projected atomic description 15, which maps elements ofthe Core Model outputs to the user interfaces 11 and the RW effectorinterface 12, and a decision surface input construction 16 whichreceives data inputs from the user input interface 11 and the RW sensorsinput interface 12 and uses an adaptive threshold, NN decision process.This decision surface construction combines these inputs intomodification of the atomic descriptions of the Core Model.

The Core Model 14 of FIG. 3 includes elements shown in further detail inFIG. 4. FIG. 4 shows a means of creating an autonomous constructionengine of an object space model for this isomorphic knowledgerepresentation which contains N dimensionality of an OD beyond theatomic RW interface attributes, in order to synthesize the complexity ofall types of encounters of the engine using non-orthogonal projectionswith a reduced set of M′ RW features. This N-D space OD is representedas point-location populations in a mathematical representation forobjects and object-links. These elements include a RW interface to thetwo input/output elements of FIG. 3 (15, 16) as an OD atomic layerinterface 17; and includes an OD-linked node representation 18 in theformat set of predicate calculus locators accessing the mass storagedevice 13 of FIG. 3. Both the RW interface 17 and the locator interface18 are constructed in a mathematical space of M and M′ dimensions, wherethis dimension (M), in general, is finite. Each of these M-dimensionalspace interfaces is mapped into an N-dimensional structurerepresentation that can be infinite and extendable from a finite space.This mapping is based on a nonlinear subspace mapping from M′→N (engine19) and M→N (engine 20) in either direction. The N dimensional space 21contains the structure of an OD represented in mathematical form. Theadvantages of this approach over the prior art are that the N-D spaceconstructs a more representative synthesis of the input data for modeledrepresentation.

The construction of the N-dimensional (N-D) object space represents theobject attributes as axes in this space of vector n, where each vectorvalue n_(i) (for i=1 to N) is the i^(th) attribute value of an objectthat spans the complete N-D dimension. Most, if not all, objects of theOD used in this space for representation consist of attributes, where iis a subset of the complete space, and hence exist in a smallerrepresentational space or subspace.

As a simplistic example, a 2-D form of an OD is shown in FIG. 5. FIG. 5shows a means of analyzing the projections of the synthesized ODrepresentation in localized subspaces for efficient metric evaluation asa STM process in the automatic refinement of the OD representation. Aset of objects 22, such as the script codes of area and length in thesignature of FIG. 2, with attributes graphically represented by a twoaxis scale, (x₁, x₂), are represented as two classes (such as developedfrom two user training sessions) by crosses for the first set, and thesecond set of objects are represented by circles. These discrete setsare based on an object classification construction from previoustraining sessions or a priori information. These objects have locationvectors d ₀ in this space and are point occupations, constructing adiscretized set of exemplars similar to those used in NN techniques. TheOD space is partially orthogonal, in order that a projection operator,{tilde over (P)}_(i) can project a “profile” of the OD onto the i^(th)axis as a density, and is shown in FIG. 5 as a histogram count of theobject density in discretized binning of the axis space. ODattribute-projected histograms are shown for both axes as P₁, P₂. Themore common 2-D representation of an OD includes a link between any twoobjects to show a relationship. In this N-D representation, the link isa second vector l _(mn) shown in FIG. 5. While the typical 2-D graphicOD has many links usually shown, for discussion purposes, only one linkis shown in FIG. 5. A second type of description, using a similaroperator to the projection operator in {tilde over (P)}_(i) objectdensity profile histograms, is used to represent link density profilehistograms as the occupation of the same discretized space with a link,projected by the link projection operator, L , and shown as a singleexample set L₁, L₂ for the x₁ and x₂ axis of the one link spanning twogrid cells in FIG. 5. Note that the link density histograms occupydiscrete grid points that are on the half spacing to the object densitygrid point spacings. In the simplest representation, the links could bethe worldline between the OD exemplars of the atomic features in thespeak/sign application. The advantages of this approach over the priorart are that the projected atomic outputs from the LTM tuple-based RDBstorage of sub-space operators have a higher fidelity in matching as RWoutputs and user accepted representations. These simple mathematicalconcepts are extended to the general mathematical concept of theinvention in the following summary.

In summary, the N-D object space description consists of o^(th) indexedobjects (with a total count O, and optional class distinction C _(o) )located with a location vector d ₀, and l^(th) indexed links (with atotal count L_(l)), indexed between m^(th) and n^(th) objects as vectorl _(mn). Projection operators operating from the complete N-D space to asingle 1-D space of axis x₁ created histograms of object density ({tildeover (P)}₁ operator created histogram density P₁(x₁) and {tilde over(P)}₂ operator created P₂(x₂)) and link density ({tilde over (L)}₁operator created histogram density L₁(x₁) and {tilde over (L)}₂ createdL₂ (x₂)). These operators can be generalized to constructions which maplarger, N-D spaces to subspaces that are M-D, where the example shown inFIG. 5 was for M=1, and N=2. Hence, the general projection operatorsproject to an M-D space as {tilde over (P)} _(m) and {tilde over (L)}_(m) , where m is the subspace dimensional set (indexed to a total of Mcount). Note that these dimensions are not necessarily sequential, asfor example with M=4, m={1,3,5,9}, and for M′=4, m={1,2,6,8}. Thesegeneralized mapping functions are shown below for an OD without classdistinction (C_(ō)=0), and a multi dimensional histogram density of mindex.

P _(m) ≡{tilde over (P)} _(m) { d _(o)} n for all object classes and n>m  (1)

L _(m) ≡{tilde over (L)} _(m) {

} n for all object classes and n> m  (2)

with l containing linked object pairs (m, n) from space n, and P _(m)and L _(m) have histogram density dimension in m.

One can also form a class filtering operation using NN decision surfacesfor the specific c^(th) class of the full class set C_(ō), where c is asubset index of ō as:

P _(m) ^(c)≡{tilde over (P)} _(m) ^(c) { d _(o)} _(n) for c subsetobject classes  (3)

L _(m) ^(c)≡{tilde over (L)} _(m) ^(c) {

} _(n) it for c subset object classes  (4)

Structure Recognition in N-D Space

The concept formation of the invention constructs an N-Dimensioned spaceof discretized point occupancy, where the axis defines an objectattribute, and the location on that axis defines the attribute value.The object attribute point location is contained in a set of objectvectors d ₀. The objects can be contained in groups of similarity asclasses, and can be filtered as class object populations from thecomplete space with subset C_(ō). The relationships between objects arecontained in a second set of vectors, as a vector between the m^(th) andn^(th) object, as link vector

. The naming of the objects is contained in the class-filtering operatorC_(ō).

The full N-D space is expandable to infinite dimensionality and hencecan contain sufficient entropy to handle any application. The “curse ofdimensionality” of this entropy is not an issue, because the populationof the space is inhomogeneous, and the linking density is more linear,rather than volumetric, such as in worm-shaped occupation densities. Theuse of a balloon matrix (see Solinsky, “A Method for Compact InformationCharacterization in a Finite, Discrete Data Set,” #JCS/ASD/93-003,Advanced Systems Division, SAIC, La Jolla, Calif. (April 1993)) to orderthis structure by spanning radiuses is another metric of the finitepopulation of the N-D space entropy. Any single axis of the space can beconsidered an atomic layer as mappable to the RW input/outputfunctionality (11, 12), or to the mass storage device LTM memory cells(13). These M-spaced cells are mapped to the N≦3 storage devicestructure using predicate calculus locators.

The invention includes a means of performing subspace mapping todimension N′<<N, and N′≧M′ for complexity representation which minimizethe OD entropy H, and hyperspace spanning distance D as a function of(N, N′, M′). Because the use of subspace projection operators on objectsand links is a form of compaction, these subspace histogram projectionsare observable discriminations for synthesizing the actual OD in itsfull dimensionality. The statistics of a histogram population densitycan be modeled from the simplest structure, to the most complex, by theuse of the Characteristic Expansion Function (see Kendall and Stuart,The Advanced Theory of Statistics, Vol. I-III, MacMillan Pub. Co.(1997)), which is a construction of ordered Am moments of thispopulation density (orders for m>2 are HOS).

The invention provides a means of utilizing HOS features in representingasymmetry for recognition metrics in the model synthesis which can beexpanded to orders beyond 8^(th) order, but are claimed operationallysuccessful at 4^(th) order. As an example, the first five moments forthe M=1 space of FIG. 5 are:

A_(o)=total count of projection density (area a)  (5)

A₁=centroid mean (μ)  (6)

A₂=centroid area normalized variance or second central moment (σ²)  (7)

A₃=centroid area normalized skewness or third central moment (m₃)  (8)

A₄=centroid area normalized kurtosis or fourth central moment (m₄)  (9)

and have been shown (see Solinsky et al, “Higher-order StatisticalApplication in Acoustics with Reference to Nonlinearities in Chaos,”Third International Symposium on Signal Processing Applications (HOSSPA92), Gold Coast, Queensland, Australia (August 1992) and Solinsky et.al, “Signal Analysis of Nonlinear Dynamics and Higher-Order Statistics,”SPIE Proceed. 2037 Chaos/Dynamics, San Diego, Calif. 163 (July 1993)) tobe useful in well characterizing RW data. A cluster object or linkdensity will eventually project to a Gaussian distribution densitydescribed by a centroid mean and variance because of approachment of thecentral limit theorem for large O or small M for large N. Two such classfiltered projections, such as the crosses (user statistic #1) and thecircles (user statistic #2) of FIG. 5, can be discriminated by using aclassification normalized distance dc defined as:

d _(c) ²≡(μ₁−μ₂)²/σ₁σ₂  (10)

where d_(c) is used to locate decision surfaces in the OD, and where theindices of the moment are based on class-filtered projections (fromEquations (3) and (4)). Distance classification techniques of the metricin Equation (10) can be expanded to complex formulations including NNforms (see Solinsky et al, “Neural-network Performance Assessment inSonar Applications,” IEEE Conference on Neural Nets in Ocean EngineeringApplications, WDC, 1 (August 1991)).

Multimodal distributions of projected densities contain higher-orderedmoments (e.g. A₃, A₄), and can be simplified using a Hermite orderingwith coefficients of skewness (normalized m₃ as S≡m₃/σ³), kurtosis(normalized m₄ with “excess” Gaussian removal as K≡(m₄/σ⁴)−3), andsuper/hyper skewness/kurtosis to 8^(th) order (see Solinsky,“Trispectrum Utilization in Higher Order Statistical Applications,”Proceedings of IEEE Conference on HOS, Grenoble, France (1991)—also inHigher Order Statistics, J. L. Lacoume Editor, Elsevier Science, Ltd,Netherlands, 339 (1992)). This cumulant statistical ordering withHermite polynomials is the preferred HOS approach for efficiency.

The identification of finer detailed structure beyond these momentrepresentations can be modeled as hyper-spheroids with asymmetry, firstas dumbbells of a single axis of spheroid coupling, and then as multipleaxis dumbbells. The amount of asymmetry is contained in the major/minoraxis eccentricity ε_(nm) between axis indices n and m, and is a pairwise spatial set. An algebraic geometric modeling can be constructed torepresent these structures, and through synthesis the projecteddensities can be represented as a geometric construction at a higherdimensionality by being projected in a manner to well match the RWpopulation. Mean-root, summed error differences can be used as a simplesynthesis feedback metric or “cost function” comparing the RW to thesynthesized RW model. A critical component in the development of thestructure formulation is contained in the use of Machine Vision (MV)techniques in analyzing these densities as images in 2-D projections.Hence, the invention also utilizes a means of incorporating MVtechnologies in assessing the symmetry of the N′ subspaces. This is anextension of the moment ordering shown previously, but representsstructure by features, which can be generalized (see Solinsky, “AGeneralized Feature Extraction Approach,” VISION '87 Conference,Detroit, Mich., 8-57 (June 1987) and Solinsky, “A Generalized ImageEnhancement for Machine Vision Architecture”, Ultratech Vision WestConference, Long Beach, Calif., 4-47 (September 1986) and incorporate NNdecision processes (see Solinsky, “Machine Vision Tutored Learning UsingArtificial Neural Systems Classification,” VISION '88 Conference,Detroit, Mich., 1-MS88-490 (June 1988)) for featuredetection/construction. It is through this analysis/synthesis process ofthe N-D representation that the core construction of the STM/LTM mappingcan be accomplished with accuracy only limited by the dynamics of theiteration in the comparison metric.

A critical element in the structure recognition is to utilize subspacesof reduced dimension, N′, where N′<<N, but N′>3, such as N′=10. Here theprojections are made to a set of “images” in reduced dimension. Theseimages are made in the same manner that FIG. 5 showed a 1-D “image”histogram “pixel” density (object/link) from N=2, and “profiles” ofthese images to 1-D, and a 2-D image pixel density from the “complete”N=3 space. However, the projections would be in the case of N′=10, froman original larger dimension of N, to a set count diversity variable(SD) of (N-N′) projections, with each expanded into a lower dimensionalspace, for a larger total SD number of “image pixel densities”, ρ( x_(m)) of (N-N′) diversity in subspace dimension, m.

The expansion of space density count to multiple subspaces of reduceddimension N′<N is shown in FIG. 6. The original example space has asingle dimension of N′=N=5, with no diversity (SD=1) as shown in FIG. 6a. There are SD=5, N′=4 dimension subspace expansions 27, with differentaxis diversity as shown in FIG. 6 b. Each of these N=4 spaces can beexpanded 28 into an N′=3 subspace diversity, with total diversity SD=5×4or shown in FIG. 6 c. Finally, for N′=2, the subspace diversity(SD=5×4×3) forms image spaces similar to FIG. 5 as shown in the output29 of FIG. 6 d. The last subspace used in the HOS metric calculation ofEquations (5) through (9) is shown in FIG. 6 e expansion 30, with thehighest diversity (SD=5×4×3×2). Thus, in general, one can see that thesubspace diversity of reduced dimensionality operates in (N-N′)-D spaceas:

SD=(N!)/(N′!) for N>N′  (11)

The entropy of this diversity can be modeled as an approximation to thefunctional definition in (O, L, {tilde over (D)}) as:

H≈SD ln (SD)  (12)

It can be shown that multi-dimensional moment density construction fordimension m, with m =(N -N′) from these subspaces, will completelyrepresent all image pixel constructions, using a weighted constructionin the same manner from Equations (5) through (9) with object populationpixel density ρ( x _(m)) as:

A ₀ ^(m)=∫ρ( x _(m))d x _(m)=normalization “area” of dimension m  (13)

A ₁ ^(m) =∫ x _(m)ρ( x _(m))d x _(m) /A ₀ ^(m)  (14)

A ₂ ^(m)=∫( x _(m) −A ₁ ^(m))²ρ( x _(m))d x _(m) /A ₀ ^(m)  (15)

A ₃ ^(m)=∫( x _(m) −A ₁ ^(m))³ρ( x _(m))d x _(m) /A ₀ ^(m)  (16)

A ₄ ^(m)=∫( x _(m) −A ₁ ^(m))⁴ρ( x _(m))d x _(m) /A ₀ ^(m)  (17)

and the similar Hermite polynomial coefficients S^(m) and K^(m) apply.This approach of the invention is a means of capturing the HOScorrelation of OD attributes in N′ dimensions, when N′≧3, with each HOSorder corresponding to an increase in dimensionality for N′ subspaces.

Hence, a mathematical process using MV central moment density algorithmsis described to construct a large feature set for quantifyingmultimodal, projected density asymmetries by subspace computation, and asynthesis feedback is used to refine the postulated to real N-D spaceoccupancy fidelity of any accuracy. This projected density of FIG. 5,described by object and link density operators of Equations (1) and (2)are seen in FIG. 5 as image “profile” pixel histograms, where the objectpixels are counted at axes locations within grid cells, and the linkpixels are counted at axes locations straddling grid cells, or at a ½grid-cell offset. These histograms can be described by a feature setbased on HOS moment expansion of Equations (5) through (9) for thesimplistic low dimension (N=2) pixel set of FIG. 5, but can also berepresented by the generalized HOS moment expansions of Equations (13)through (17) to fourth order, and are anticipated to not requirerepresentation beyond eighth order, but in principal can extend to allorders. This set of HOS becomes the feature set used for deciding uponthe accuracy of the OD representation. This approach of the inventionprovides a means of continuously expanding the OD dimensionality to adimension of the OD of N≧N′ where N can be infinite, based on a fidelitycontrolling parameter in the synthesis metric decision threshold dparameter. This advances prior art to unique levels of complex modeling.

Thus, a feedback process is constructed within the space to synthesize adimensional space of order N>M′ to represent the observed ordering ofthe RW interface atomic dimension being limited by the interfaceobservable counts of input/output devices in (11) and (12) of FIG. 3.The N representation space is expanded and populated with random datapixels, and then iterated and compared with the subspace projectionusing the HOS metrics. The expansion and projection synthesis does nothave to be sequential as shown in FIG. 6, nor does it have to use fullyorthogonal (i.e. eigenvector decomposition based on Principal ComponentAnalysis) projections. It can utilize partially projected axesorthogonality, as used by Independent Component Analysis techniques (seee.g. Sejnowski and Churchlang, The Computational Brain, MIT Press,Cambridge, Mass. (1992) and Lee, Independent Component Analysis-Theoryand Applications, Klumer Academic Publication, Boston, Mass. (1998)).The HOS features and metric comparisons will still carry all of thediscrimination. These projections can also be of fractal order to axesof partial correlation. In this manner, a completely orderedrepresentation space is possible, and extendable to N=∞, with diversityin the subspace, SD, being quite large, yet finite (N−N′≠∞), no matterhow complex the atomic layer becomes, where eventually, N′=M′ satisfiesthe RW mapping dimensionality using a variety of N′ examples. Thesimplification of this synthesis construction is contained in anonlinear and nonsequential search strategy, using the principles of theAC, that optimally discovers the required object- and link-pixelrepresentation for the RW example data at hand.

Neural-network (NN) Terminal Cycle (TC) Decision Surface Classification

An element in simplifying the N-D space construction and in creatingasymmetric projections is to use the class filter in the projectionoperator as described by Equations (3) and (4) and construct decisionsurfaces with NN technology. This increases the pixel complexity, from aCentral Limit Theorem growth to second-order statistics with noasymmetry metric, to a more complex asymmetric projection. Classconstruction requires decision surface formation in the M-D space.

Solinsky et al, “Neural-network Performance Assessment in SonarApplications,” IEEE Conference on Neural Nets in Ocean EngineeringApplications, WDC, 1 (August 1991) has shown the superiority of NNdecision surfaces for class surface constructing over the simpledistances formulations used in Equation (10) for bimodal separation.This superiority is due to the nonlinear surface construction over thelinear form of Equation (10). NN decision surfaces are able to includesurfaces of quadratic with one hidden layer, and hyper-quadratic withtwo hidden layers (see Lippman, “An Introduction to Computing withNeural Nets,” IEEE Acoustics, Speech and Signal Processing Mag. 4 (2),4-22 (April 1987)), implying that the N-D classification layers would bebeyond two hidden layers in complexity. A second means of decisionsurface construction, which can create separation of highly overlappingclass populations, uses a dynamic nonlinear sequence technique (seeGriffith, Mathematical Neurobiology, Academic Press, NY, N.Y., Chpt. 8(1971)) of neural biological origins. This approach is also applicableto the application, because it constructs a finite set of random stepsin a sequence network of the i^(th) state, using a step operator S(i).Here, the sequence operator is sampling the population density at anylevel of dimension in subspace N′, with the next step sample address(location vector S _(n) for | n|=N′) derived from the operation of theuse of the density value as a seed entry into an address table of randomvalues. By using random noise dithering, with sampling of neighboringdensities, this approach can be shown to generate a stable set ofaddresses after a logarithmic number of iterations (Y) on thedimensionality. This is called a terminal cycle (TC) decision mappingand has been shown to be stable to large dimensionality (N=10⁶, Y=1253,see the Griffith article).

An important part of a successful TC approach is to populate the emptyN-D space grid points with random noise for dithering, such that emptypoints will still generate a different mapping address each time. In theTC approach to decision surfaces, the classification structure iscontained in the sequence of the address jumps, which can be thought ofas just the link vectors between the object exemplar population. That isto say, the address sequence in the TC approach becomes the link-listworm hole density, as described in the worldline example. Hence, theinherent ability to represent “knowledge” in this N-D space as a set ofobjects and links through class filtering has been simplified to classregional object mapping with NN surfaces contained in the weights ofeach g^(th) hidden layer, and the addresses of the sequence of linkvector lists constructs from links l _(mn). In essence, the inventionhas replaced an object/link density moment model with a sequence list ofaddresses based on class decision surface projections. The final elementof the invention is the initiation of the N-D space for a givenapplication, and the method of auto-construction for timely utilizationof the N-D knowledge representation. The multimodal separation fromprojected density asymmetry moment expansions is the critical beginningpoint of a priori learning for the NN and uses a gas/liquid dynamicmodel. This model, described next, uses a dynamic stability ofcontinuously changing representations.

Initiation and Dynamic Stability

Referring to FIG. 3, the application of the invention is in obtaining aSTM model for M′- D representation of atomic input/outputs from the RWinto an N-D OD model. However, it is recognized that the STM willrequire access to expanding spaces as the application changes its RWinputs, and hence the N-D OD model must be remappable back to LTMstorage of M dimensions. This migration of an N-D OD to M-D LTM storedin a mass storage media is based on a continuous M′- D RW input/outputstimulation. This LTM mapping is based on a finite set of class locationstructures of the dynamic address sequence set, and is loadedinto/out-of the STM N-D space density representation, using predicatecalculus location indexing. Hence, the multiple 2-D structure of the LTMis based on an address sequence for each class (2-D), for each subspacerepresentation. The stability and finite time for LTM memory populationaccess must critically use a subspace construction in order to limit allfidelity levels of searches. Very fast LTM models can be constructed atlow level fidelity, and can be refined with the addition of expandeddimensions about the initial representation as described next.

The synthesis of STM and mapping to LTM involves taxonomy of anisomorphic relationship between the N′ subspaces to the M≦3 storagespace of the LTM media. Each subspace dimension has been shown to bemappable down to N′=2 or 3 spaces, and hence has a direct means ofstorage in LTM. It also has a means of “tagging” the diversity for eachof the SD iterations within this subspace dimension. Hence, the LTMconstruction taxonomy is considered to be a class sequence in N′representations, with a large linked list of SD diversities. This allowsfor expansion of subspace fidelity within a single N′ representation byadding more “platters” to the LTM. These 2-D physical platters form alinked-list construction. Because this is an object class model, themapping can be directly controlled by the LTM search algorithms using apredicate calculus representation, and avoids any indirect LTMaddressing. This is an important element of the efficiency for the STMsynthesis search as the application requires higher fidelity (increasingSD) and higher dimensionality (increasing N′) for maturing orstabilizing dynamics of the RW knowledge representation.

A continuous process in the construction of LTM is the construction ofclass decision surfaces in STM that is efficient for a given level ofuser fidelity. It is the class filtering process which creates the HOSfidelity in the subspace projections. Here the use of forced choice userdecision feedback in an AC are used to quickly identify the subspace forthe decision surface construction, and the merging or elimination ofother subspace diversities. A model of the classification dynamicscombines two previous concepts to retain a dynamic classification forthose of a) large regional density representation, such as a liquidregion with a parametric density in ρ( x _(m)) encompassing all pixelgrid points, and b) singularly-narrow regional density representations,such as a gas region with a discrete set of pixels as a pixel densityused to represent ρ( x _(m)) for RW exemplars. This allows for aninitiation of the invention by letting N′=M=N for an initial set of RWobjects. Exemplars replace the zeroth state value of random values inthe STM during initial user interaction.

The system iterates with N>N′, with increasing diversity maintainedinitially by class heuristics being used, either as operator heuristicsor user profiles determined from the application. As gas regions becomemore dense and frequently accessed, the modeled classification migratesto a liquid model, with a gas model used near the decision surfaces. Asthese regions of liquid modeling become inactive, they migrate back topixels of recent exemplars as a gas model. This continuous actiondynamic through RW stimulation modifies the STM representation, as wellas the LTM migration. The use of TC decisions is a means of continuouslytesting regions of the N-D space for activity or utility withoutexhaustive search approaches. This becomes a means of implementing theAC formulation for equivocation and uncertainty and hence retainscontinuous RW user feedback to the utility and applicability of theLTM/STM states. The constructed STM and LTM representation of user(s)knowledge in this dynamically-classed N-D space allows the invention tosupport a large variety of decision-process applications, where either achosen input attribute set is compared as a test to decisively match aclass, or an object location is projected for an output attributecontrol to perform a function (e.g. the “Grandmother” neuron exampleused in recognizing a picture of one's grandmother).

As a further illustration of the invention, the example application ofFIG. 2 is described using the RW interface of a personal PDA hand heldcomputer (a machine, such as shown in the FIG. 1 concept) connected viaa wireless communication link to a large server via a LAN or theInternet. The PDA displays data output in graphic and alphanumeric form,and receives data input in the form of spoken words and “pen”scratchings on the display surface, for example, using a typicalPalm-Pilot-like device. One can also add a location RW device forexternal non-user information, such as a commercial GPS RF receiver.This constitutes a single application realization of the user and sensorinterfaces 11, 12 of FIG. 3.

Inside the PDA is an ASIC, which is based in the invention's technologymodels and algorithms, and represents the STM contained in elements 14,15 and 16 of FIG. 3. The Core Model 14 of FIG. 3 is the essence of thealgorithms in the ASIC, with the interface mapping 16 and projection 15algorithms utilizing computational storage contained in the ASIC andmass storage devices. The link to the mass storage device 13 is the LTMof FIG. 3, and is accessed using simplistic predicate calculus commandsas messages over the PDA wireless communication. This mass storagedevice can be a server, which returns RDB types of specific LTM subspacepopulations for rework by the STM. Note that here the RDB accessing ofLTM avoids previously cited problems in the use of SQL for RDB usage.This particular application is shown in FIG. 7 for user authenticationand identification in the e-presence application of FIG. 2.

The utilization of this application device is as follows:

-   1) The LTM 31 is used as a remote server through a telecommunication    interface link (i.e. telecom 36 in FIG. 7 to support the STM 32),    limited by the ASIC resources of the STM realization, through    dynamic, but frequent and discontinuous access to a RDB storage    using a predicate calculus algorithm. There, previously captured    profiles of the user's behavior derived from preferences, correlated    events, etc. (e.g. e-presence signature) are stored.-   2) The graphic user interface 33 uses a display of characters on the    PDA screen in a sequential, and rapid manner, with random location    and value, and having the display time limited to forced-choice    cognitive science values (e.g. 5 per second).-   3) The atomic feature input space of M′=10 is made with commercial    voice characterization (e.g. LPC) sampled at forced choice response    times (e.g. 5 per second), and sequential handwritten features based    on MV analysis of the pen scrollings at a similar forced choice    sampling (e.g. 5 per second on the temporal GUI input sequence). The    dimensions of this input representation could be based for example    on six LPC features for voice input and four MV features for hand    written input, for a six second session of user ID input    verification (37) comprising a worldline identity in the N=10    correlation space.-   4) User ID authentication of the e-presence could be expanded to    include location-dependent ID for further dimensional correlation,    and begins with a location-dependent (GPS 34) selection (12 of    FIG. 3) of LTM space exemplars (13 of FIG. 3). The user is notified    graphically (33, 15 of FIG. 3) to sign one's name with the pen,    while simultaneously speaking the same user name. The STM (32, 14 of    FIG. 3) clocks (35) the sampling of the input data to a set of 30 (6    sec/200 msec) sequential input feature vectors of atomic attributes.    These vectors construct a linked set of sequential vector subspaces    (N′≧10) for a diversity of potentially large dimensions (SD≧30).    Based on Equation (21), the STM representation could be as large as    N≧12 for a (N-N′)=3 limitation at the atomic level. However,    previous training of the LTM by the user (a few examples), would    construct class representation of this diversity to potentially only    a few subspaces, varying with user location.-   5) An adaptive threshold d (set by the designed Pc level) in the    test user ID example (of name signing with simultaneously speaking    the name as speak/sign) is made in an N=10 correlation space, which    has exponentially increasing reliability to simple N=2 space    correlation. In fact, it is the proper sequence and user action,    which creates a single link of correlation structure of the    exemplars as a worldline to which the test sample is compared with    in LTM training storage, much as is done in the TC representation.    This feature input concurrency is an important element of achieving    the large Pc values.-   6) Upon “correct” user ID recognition, the PDA further verifies the    user ID through a second test, by displaying a sequence of letter    and numbers for six seconds in a forced choice mode clocking (38),    with the user verbally stating each number or letter. Again, a    subspace of the (N′≧10) atomic input features is used (without the    pen inputs) for recognition, such as for N′=6 from before, or also    could be expanded to N′>10 if higher fidelity is required.

This application is a simplistic representation of the STM/LTMpartitioning and user/sensor interfaces, with the Core Model containedin an ASIC. The ASIC algorithm operates using a representation of thesubspace atomic inputs, expanded by synthesis to create recognitionfidelity for class decisions. This expansion adds subspaces and classes,with dynamic control of recognition within the invention technologyapplication product provider, who desires the user ID testing results.This control limits the extendibility of the Core Model algorithm in HOSfeature order (fourth or more of Equation (13) through (17)) and indiversity computation order (m of Equation (13) through (17)).

If N′ is limited to a higher dimension through optimum synthesis, thenthe STM represents the recognition correlation very quickly as an n=N′spatial feature set. Non-optimum implementation of the Core Model willresult in longer convergence times of n<N′ and more extensive diversity.

The invention is applicable to all problems in complexity, such asexemplar population and attribute normalizations, which requireextensions to N>3 spaces for representation, and can be shown to beextendable to N=∞ without an infinite time required for synthesis, dueto the linear nature of the TC and worldline approach. This is becausethe representation is limited in fidelity and model hyperspaces withlinear growth metrics, and hence always deals with a collection ofsubspace decision surfaces based on class projections, in order toachieve a higher order correlation analysis. The use of heuristics anduser profiles allows for the sharing among a set of users of LTM spacesto more rapidly initiate the system use, and reduce user training timefor forming the exemplar population in a minimum time.

Finally, because of the dynamic user interaction provided by theliquid/gas model of class decision surfaces, (which allow for thesynthesis process to proceed into a rapid convergence), the inventioncan change with all types of variations based on the RW environment fromthe user and sensors of the input, and the control of this environmentis monitored by the forced-choice sampling of the output effections.

Example Method and Systems for Applications

An example method is shown in a flow chart in FIG. 8. The user and/or RWsensor inputs begin to stimulate and a response to this stimuli isconstructed (40) by first capturing this stimuli (40) and storing it,and successive stimuli (41) into the STM element (42). Here a synthesisof the OD model construction is performed and the model abstraction isstored (43) into LTM (44). During synthesis (42), or as a result of newstimuli (39), the LTM (44) is used to retrieve the synthesized model(45) and either a new model is synthesized as a dynamic to furtherstimuli (42, 39, 40) or an output response is generated (46) and drivesthe user and/or RW effector outputs (47). The dynamic synthesis can alsorequest new user and/or RW input (39) as requested by the synthesis (46)of the STM (42). Specific functions of this method embodied in the textare listed in FIG. 8.

An example system which creates the implementation of this method ofFIG. 8 is shown in FIG. 9 as a generic block diagram. The user and/or RWinput interface 48 is based on commercial standards and components, andinterfaces to a data capture device 49 embodied as either a process orspecialized board, an ASIC, or a net-list which provides the STMcomponent 50 with input data as a stimuli. The STM utilizes afloating-point processor component with memory to scale the input datavalues to fixed point storage for use in the model synthesis process.The model synthesis occurs in the STM components 50 which execute fixedpoint processors and data value arithmetic operations, driven by aprocessor and memory. The STM controls this synthesis by using a controlprocessor operating on the synthesis parameters and creating testingdecision outcomes that are stored in memory. This control processor usesa synthesis processor code component that orchestrates the synthesisprocess and stores and retrieves results with a mass storage LTM devicethrough a processor, memory, and communication device 54, with the LTMbecoming as an example, a commercial RDB system 51. The results of thedynamic model synthesis and control process, based on the input stimuli48 are outputted through a data generator device 52 which conveys outputdata for display for the user and/or used by the RW output effectors 53.These generic components of FIG. 9 can be embodied in a system ofcommercial devices and components, a processor executing software withexternal input/output devices and mass storage devices, an ASIC devicedesigned with components of input/output interfaces to other commercialsuppliers, or as a net-list for use by other commercial suppliers whichis incorporated into specific application hardware product systems.

Each of the documents listed above is incorporated herein by reference.

While particular embodiments of the present invention have beendescribed and illustrated, it should be understood that the invention isnot limited thereto because modifications may be made by persons skilledin the art. The present application contemplates any and allmodifications that fall within the spirit and scope of the underlyinginvention discloses and claimed herein.

1. A method of generating outputs in response to real world stimulationcomprising: capturing concurrent inputs that are responsive to trainingstimulation; storing a model representing a synthesis of the capturedinputs; and using the stored model to generate outputs in response toreal-world stimulation.
 2. The method according to claim 1, furthercomprising: using a forced choice interaction to generate one or moreadditional inputs; capturing the additional inputs; and incorporatingthe additional inputs into the model.
 3. The method according to claim1, wherein the model comprises a worldline of linked object diagramexemplars in an N-dimensional space.
 4. The method according to claim 1,wherein the real world stimulation comprises concurrent inputs that arecompared to the stored model, and the outputs are based on the resultsof the comparison.
 5. A computer readable medium for storingcomputer-executable instructions for performing the method of claim 1.6. A hardware processing engine configured to perform the method ofclaim
 1. 7. An application specific integrated circuit configured toperform the method of claim
 1. 8. A net list integrated into otherintegrated circuits to perform the method of claim
 1. 9. A method ofgenerating outputs in response to control command stimulationcomprising: capturing concurrent inputs that are responsive to trainingstimulation; storing a model representing a synthesis of the capturedinputs; and using the stored model to generate outputs in response tocontrol command stimulation.
 10. The method according to claim 9,further comprising: using forced choice interaction to generate one ormore additional inputs; capturing the additional inputs; andincorporating the additional inputs into the model.
 11. The methodaccording to claim 9, wherein the model comprises a worldline of linkedobject diagram exemplars in an N-dimensional space.
 12. The methodaccording to claim 9, wherein the real world stimulation comprisesconcurrent inputs that are compared to the stored model, and the outputsare based on the results of the comparison.
 13. A computer readablemedium for storing computer-executable instructions for performing themethod of claim
 9. 14. A hardware processing engine configured toperform the method of claim
 9. 15. An application specific integratedcircuit configured to perform the method of claim
 9. 16. A net listintegrated into other integrated circuits to perform the method of claim9.
 17. A system for generating an outputs in response to real worldstimulation comprising: input capture circuitry that captures concurrentsystem inputs that are responsive to training stimulation; a memory thatstores a model representing a synthesis of the captured inputs; and anoutput generator that uses the stored model to generate outputs inresponse to real world stimulation.
 18. The system according to claim17, wherein the input capture circuitry further captures one or moreadditional inputs generated from a forced choice interaction and theadditional inputs are incorporated into the model.
 19. The methodaccording to claim 17, wherein the model comprises a worldline of linkedobject diagram exemplars in an N-dimensional space.
 20. The systemaccording to claim 17, wherein the real world stimulation comprisesconcurrent inputs that are compared to the stored model, and the outputsare based on the results of the comparison.
 21. The system according toclaim 17, wherein at least part of said system is implemented in acomputer software program.
 22. The system according to claim 17, whereinat least part of said system is implemented as a hardware processingengine.
 23. The system according to claim 17, wherein at least part ofsaid system is implemented as an application specific integratedcircuit.
 24. The system according to claim 17, wherein at least part ofsaid system is implemented as a net list integrated into otherintegrated circuits.
 25. A system for generating an output in responseto control command stimulation comprising: input capture circuitry thatcaptures concurrent system inputs that are responsive to trainingstimulation; a memory that stores a model representing a synthesis ofthe captured inputs; and an output generator that uses the stored modelto generate outputs in response to control command stimulation.
 26. Thesystem according to claim 25, wherein the input capture circuitryfurther captures one or more additional inputs generated from a forcedchoice interaction and the additional inputs are incorporated into themodel.
 27. The method according to claim 25, wherein the model comprisesa worldline of linked object diagram exemplars in an N-dimensionalspace.
 28. The system according to claim 25, wherein the real worldstimulation comprises concurrent inputs that are compared to the storedmodel, and the outputs are based on the results of the comparison. 29.The system according to claim 25, wherein at least part of said systemis implemented in a computer software program.
 30. The system accordingto claim 25, wherein at least part of said system is implemented as ahardware processing engine.
 31. The system according to claim 25,wherein at least part of said system is implemented as an applicationspecific integrated circuit.
 32. The system according to claim 25,wherein at least part of said system is implemented as a net listintegrated into other integrated circuits.